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For Review OnlyChanges in millennial adolescent mental health and health
related behaviours over ten years: a population cohort comparison study
Journal: International Journal of Epidemiology
Manuscript ID IJE-2018-08-1015.R1
Manuscript Type: Original Article
Date Submitted by the Author: n/a
Complete List of Authors: Patalay, Praveetha; University College London, Gage, S; University of Liverpool
Key Words: depression, smoking, alcohol, sleep, ALSPAC, Millennium Cohort Study
For Review Only
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Changes in millennial adolescent mental health and health related behaviours over
ten years: a population cohort comparison study
Praveetha Patalay*1,2, Suzanne H Gage1
1Institute of Psychology, Health and Society, University of Liverpool, Bedford Street South,
Liverpool, UK
2 Centre for Longitudinal Studies and MRC Unit for Lifelong Health and Ageing, UCL, Gower
Street, London, UK
Word count: 4116
*Correspondence to:Praveetha Patalay,Centre for longitudinal studies,20 Bedford WayLondonTel: 02076126051Email: [email protected]
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Abstract
Background: There is evidence that mental health problems are increasing and substance
use behaviours are decreasing. This paper aimed to investigate recent trends in mental ill-
health and health-related behaviours in two cohorts of UK adolescents in 2005 and 2015.
Method: Prevalences in mental health (depressive symptoms, self-harm, anti-social
behaviours, parent reported difficulties) and health related behaviours (substance use,
weight, weight perception, sleep, sexual intercourse) were examined at age 14 in two UK
birth cohorts; Avon Longitudinal Study of Parents and Children (ALSPAC, N=5627, born
1991-92) and Millennium Cohort Study (MCS, N=11318, born 2000-02). Prevalences and
trend estimates are presented unadjusted and using propensity score matching and entropy
balancing to account for differences between samples.
Results: Depressive symptoms (9% to ~15%) and self-harm (11.8% to ~14.5%) were
higher is 2015 compared to 2005. Parent-reported emotional difficulties, conduct problems,
hyperactivity and peer problems were higher in 2015 compared to 2005 (5.7 – 9% to 9 –
18%). Conversely, substance use (tried smoking 9% to 3%; tried alcohol 52% to ~43%,
cannabis 4.6% to ~4%), sexual activity (2% to ~1%) and anti-social behaviours (6.2 – 40.1%
to 1.6 – 28%) were less common or no different. Adolescents in 2015 were spending less
time sleeping (<8 hours 5.7% to 11.5%), had higher BMIs (obese, 3.8% to ~7.3%) and a
greater proportion perceived themselves as overweight (26.5% to 32.9). The findings should
be interpreted bearing in mind limitations in ability to adequately harmonize certain variables
and account for differences in attrition rates and generalizability of the two cohorts.
Conclusion: Given health-related behaviours are often cited as risk-factors for poor mental
health, our findings suggest relationships between these factors might be more complex and
dynamic in nature than currently understood. Striking increases in mental health difficulties,
BMI and poor sleep related behaviours highlight an increasing public health challenge.
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Keywords: depression, smoking, alcohol, sleep, ALSPAC, Millennium Cohort Study,
adolescence
Key messages
- Large increases are observed in some mental health difficulties (depression, self-
harm), obesity and poorer sleeping habits in the ten years between 2005 and 2015,
while antisocial behaviour and substance use seem to be decreasing or are
unchanged.
- Given health-related behaviours are often cited as risk-factors for poor mental health,
our findings suggest relationships between these factors might be changing over time
- The findings have important implications for policy and public health planning related
to mental health and substance use.
- Our findings also present data on changes in sleep behaviours and weight
perceptions, highlighting the need for further research into the role these behaviours
might play in the rising mental health difficulties observed in today’s adolescents.
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Introduction
The focus on adolescent health has been increasing in recent years,1 with a growing
recognition that these years are pivotal in the development and maintenance of health
behaviours and outcomes through the lifecourse.2, 3 Adolescence is a key period for mental
health disorder onset with half of lifetime onset by age 14.4 Research over previous decades
suggests that the prevalences of mental health problems are increasing in UK teenagers,5, 6
which is mirrored in studies across different countries.7, 8 An international systematic review
investigating secular trends in adolescent mental health from the previous century into the
start of this century concluded that internalizing symptoms seem to be increasing, finding
more consistent evidence for increases in girls compared to boys.9 Most studies in this
review focussed on internalizing symptoms or general psychological distress, making
conclusions about externalising behaviours less possible. There are few studies comparing
changing trends in the millennial generations, and prevalence studies suggest that mental
health problems in mid-adolescence might have increased even further in recent years.10 11
In contrast, while prevalence of internalizing mental health problems seems to be increasing,
young people in the UK are becoming less likely to be underage substance users. Office for
National Statistics reports collected from secondary school pupils in England have found
prevalence of alcohol use, smoking, cannabis use and other drug use among 14 year old
pupils have consistently fallen since 1982 when the survey was first undertaken.12 For
example, in 1982 16% of 14 year olds described themselves as regular smokers. In 2014
this had fallen to just 4%, and the drop was consistent across genders. This decrease in use
has become particularly pronounced since the early 2000s.12
Given that various health behaviours including but not limited to substance use are
implicated in risk for poor mental health,13-18 investigating the relationships between these
secular trends is important to explore causal relationships, the aetiology of mental ill-health,
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and potentially to inform interventions to try and reverse the increasing prevalence of mental
health problems. It is therefore surprising that to date there has been little attempt to
combine investigations of secular trends in mental health with changes in health related
behaviours. We also know little about trends in other health related behaviours such as
sleep, risky sexual behaviour, body satisfaction and physical activity that might also be
causal risk factors for mental ill-health and substance use.13-18
In the current study we use two cohorts of UK adolescents born a decade apart (1991/92
and 2000/02) in order to identify changes in mental health, considering both internalizing and
externalizing symptoms, and a number of related health behaviours including substance use,
sleep behaviours, weight, physical and sexual activity. In particular we attempt to make the
variables and datasets as comparable as possible by harmonizing the variables and
performing two different techniques (propensity score matching and entropy balancing) to
increase the comparability of the cohorts. The prevalence of a number of these behaviours
differ between males and females, and some studies report different trends in males and
females.6 We therefore also empirically examine sex differences in changes over time in
these outcomes.
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Method
Participants
Avon Longitudinal Study of Parents and Children (ALSPAC) is a cohort born in 1991-92.
ALSPAC recruited 14,541 pregnant women resident in Avon, UK with expected dates of
delivery 1st April 1991 to 31st December 1992. When the oldest children were approximately
7 years of age, an attempt was made to bolster the initial sample with eligible cases who had
failed to join the study originally. The total sample size for analyses using any data collected
after the age of seven is therefore 15,247 pregnancies, resulting in 15,458 foetuses. Of this
total sample of 15,458 foetuses, 14,775 were live births and 14,701 were alive at 1 year of
age.19, 20 The study website contains details of all the data that is available through a fully
searchable data dictionary and variable search tool
(http://www.bristol.ac.uk/alspac/researchers/our-data/). Ethics approval for the study was
obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics
Committees. Data were collected frequently via different modalities, with clinic visits and
postal questionnaires having taken place in adolescence every year. This study uses data
from ages 13, 14 and 15. In the current study, data were available from 6132 participants at
age 14 representing 41.7% of the 14701 participants alive past 1 year. Attrition is predicted
by a range of variables in ALSPAC including lower educational level, male gender, non-
White ethnicity, and eligibility for free school meals.19
Millennium Cohort Study (MCS) is a cohort of 19,517 children born in 2000-02 sampled
from the whole of the UK.21 Data so far have been collected in 6 sweeps at ages 9 months,
3, 5, 7, 11 and 14 years. The study website (https://cls.ucl.ac.uk/cls-studies/millennium-
cohort-study/) contains details regarding all the data available and information on accessing
the datasets. Ethics approval for the age 14 sweep was obtained from the National
Research Ethics Service Research Ethics Committee. At the age 14 sweep, 15415 families
were issued into the field (those not issued due to emigration, permanent refusal,
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untraceability), of which 11726 families participated in the age 14 sweep (representing
60.9% of the original sample).22 Attrition at the age 14 sweep compared to the full sample is
predicted by a range of demographic variables including male gender, Black ethnicity, lower
occupational and educational level and single parent family.23
For this study, we analysed data from participants who had provided data on at least one of
the outcome variables at the age 14 sweeps of the studies (depressive symptoms, smoking,
alcohol, cannabis and other drugs; ALSPAC N= 6132, MCS N= 11351). Furthermore,
participants without the demographic data required for increasing the comparability of the
datasets (sex, ethnicity, age, maternal education and maternal age) were excluded, resulting
in an analysis sample of 5627 from ALSPAC and 11318 from MCS.
There have been changes in socio-demographic characteristics of the country in the ten
years between these cohorts (e.g. higher proportion ethnic minorities, higher education
levels) and in addition the two cohorts represent different regions that might have different
characteristics with ALPSAC being a regional and MCS a national cohort. For instance,
around one-fifth of the MCS sample are ethnic minorities compared to around 4% in
ALSPAC. To minimise the bias in estimates that socio-demographic differences in the
samples might cause, we control for socio-demographic factors in analysis and in addition
employ two additional approaches (propensity score matching and entropy balancing) to
increase the comparability of the cohorts.
Measures
The measures used in this study (Table 1) include socio-demographic indicators (used for
increasing cohort comparability), mental ill-health (depressive symptoms, self-harm, parent
reported difficulties), substance use (alcohol, smoking, cannabis and other drugs), antisocial
behaviours (assault, graffiti, vandalism, shoplifting and rowdy behaviour) and other health
related behaviours (including sleep, weight, weight perception and sexual activity). In a few
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instances (self-harm, sleep behaviours, parent-rated difficulties), the variables of interest
were not available in ALSPAC at age 14, but available in the sweep immediately before (age
13) or afterwards (age 15) and where this is the case is clearly indicated in the table. Table 1
also presents the details of the harmonised variables that were subsequently used in
analysis. Some of the variables were more readily comparable than others, for instance both
studies used the Short Moods and Feelings Questionnaire24 to assess depressive
symptoms, parent-rated Strengths and Difficulties Questionnaire25 to measure difficulties and
the same set of questions to record sexual activity. Other variables were harmonised
through a process of creating new, comparable variables across the datasets (e.g. alcohol,
smoking). For self-harm, even after harmonisation the resulting variable is not truly
comparable due to different time scales of the question asked, which needs to be borne in
mind when interpreting findings. Lastly, some health related behaviours that we planned to
harmonise and investigate (physical activity) were substantially differently measured and
harmonised measures could not be derived.
Analysis
Increasing comparability of the datasets
To increase comparability of the samples by accounting for key socio-demographic
differences between these samples, participants from the larger MCS sample were matched
or weighted to make them comparable to the ALSPAC sample on key demographic factors
including sex, age, ethnicity, maternal education and maternal age at birth. This was done
using two approaches: propensity score matching26 and entropy balancing.27 Both
approaches aim to reduce the probability that differences between samples on outcomes of
interest are because of sample differences on relevant demographic variables.27 Table S1
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shows the differences in these characteristics in the samples before and after these
procedures were applied.
Propensity score matching is based on a propensity score, which is derived from weighting
schemes based on the criteria that are to be matched to identify individuals from the larger,
control group that are most like each of the individuals in the treatment group (in this case
ALSPAC) across a range of variables as specified. Propensity score matching was
conducted in STATA using psmatch2.28
Entropy balancing is a multivariate reweighting method that calibrates unit weights such that
two samples are balanced on a range of pre-specified variables, hence increasing
comparability for the estimation of treatment, or in this case cohort, effects.27 The application
of this approach creates an entropy balancing weight value for all participants in the MCS
sample, which is then used as a weight when estimating prevalences in the MCS sample.
This approach allows the utilisation of the full available MCS cohort, instead of selecting a
matched sub-sample like the propensity score matching approach. Entropy balancing was
conducted in STATA using ebalance.29
Missing data
In ALSPAC 15.6% of the total cells were missing (ranging from <1% for substance use,
1.8% for mental health, ~24% for antisocial behaviours and ~26% for sleep behaviours). In
the MCS samples, 1% of cells were missing in the MCS propensity matched sample and
1.2% in the full MCS sample. Multiple imputations (20 imputations) were carried out using
chained equations separately in the two cohorts.
Estimating cohort differences
Four estimates (ALSPAC, MCS nationally representative, MCS propensity matched and
MCS entropy balanced) of the prevalences and descriptive statistics (means and % with
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95% CIs) for each of the harmonised outcome variables were first estimated. In addition, we
estimated odds ratios (Figure 1) of the cohort effects (MCS compared to ALSPAC) for the
prevalence of high mental health or risky health behaviours using logistic regressions. Lastly,
we also examined sex into cohort interactions with the ebalancing weight to examine
whether any outcomes different extent of change in males and females.
For ease of interpretation for the reader, throughout the rest of the paper we refer to the
ALSPAC variables year of collection as 2005 and the MCS variables as 2015.
Results
There were no differences between the samples in sex distribution and maternal age at birth
(Table S1). Regarding the other characteristics, as expected, MCS had higher proportions of
ethnic minorities and higher levels of maternal higher education. Of the two approaches
used to increase comparability, the propensity matching resulted in the two samples
becoming more similar, for example ethnic minorities were less than 4% in the ALSPAC
cohort compared to more than 20% in the full MCS cohort, while the propensity matched
MCS cohort consisted of around 10% of minority ethnic individuals. In contrast, the entropy
balancing (based on the generated entropy weights) resulted in matched estimates across
demographic characteristics in the two cohorts.
Estimates from the propensity score matched sample and the entropy balancing in the MCS
were very similar in most cases and for most outcomes, different from the MCS nationally
representative estimates, indicating the relevance of adjusting the estimates when
estimating cohort differences. The descriptive statistics indicated that there were more young
people with mental health problems, as indicated by greater proportion above depression
threshold and reporting self-harming, in 2015 compared to 2005 (but note the self-harm
behaviour question was limited to past 12 months in 2015 compared to lifetime in 2005).
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Antisocial behaviour and substance use rates were lower in 2015 compared to 2005. Parent
reported difficulties highlighted higher rates of emotional, conduct and hyperactivity
symptoms and greater levels of problems getting along with peers in 2015 compared to
2005. With regards to the health-related behaviours, the more recent cohort had a higher
BMI on average and larger numbers also perceived themselves to being overweight. The
data on sleep behaviours indicated that on weekdays young people in 2015 were more likely
to sleep later and more likely to wake up earlier. Weekend sleep and wake times were more
similar between the cohorts. A greater proportion of adolescents in 2005 reported having
had sexual intercourse by this age compared to in 2015. Due to the higher comparability and
complete sample size using entropy weights and the similar estimates produced with entropy
and propensity adjustment, entropy balancing is used for subsequent regression analyses
comparing the two cohorts and the sex by cohort interactions.
Figure 1 illustrates odds of outcomes in the MCS sample (2015) compared to the ALSPAC
sample (2005) using both a direct comparison approach and using estimates applying the
entropy balancing weights. Estimates were similar for most of the mental health and some
health related behaviour outcomes based on the two approaches, but there was some
noticeable upward or downward bias for some outcomes, for instance with entropy balancing
the lower odds in 2015 compared to 2005 are more stark for antisocial and risky health
behaviours; highlighting the potential relevance of using methods to increase the
comparability of cohorts when estimating cohort differences.
Descriptives stratified by sex are presented in Table 4. Depressive symptoms, self-harm,
overweight perception were higher in females and antisocial behaviours, peer problems
higher in males., Regression analysis with the entropy balancing weight were estimated to
examine sex-by-cohort interactions. Most health-related behaviours showed little or no sex
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differences in prevalence. There were no sex-by-cohort interactions for most of the
variables included in this study, indicating that rates of change or increased/decreased odds
were similar in males and females. There was evidence of sex differences in cohort effects
for some antisocial behaviours (e.g. assault ORmale = 0.66, ORfemale = 0.45), parent-reported
conduct problems (ORmale = 2.74, ORfemale = 1.38) and having tried alcohol (ORmale = 0.85,
ORfemale = 0.59), where odds of these behaviours in 2015 compared to 2005 were lower in
females compared to males (irrespective of whether overall odds were lower or higher in
2015). Odds ratios separately by sex were estimated and presented in Figure 2.
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Discussion
The current study examined changes in a range of mental health and health related
behaviour outcomes in mid-adolescence over ten years (2005 to 2015) using two key UK
birth cohort studies. Importantly, the study investigated this range of outcomes within the
same analytic framework, and employed methodological techniques to provide comparable
estimates across the different health outcomes.
Prevalence of depressive symptoms, self-harm and parent reported mental health difficulties
were all higher in 2015 compared to 2005, whereas anti-social behaviours were lower in
2015. Changes in these mental health outcomes were substantial, with a 6% increase (9% in
2005, 14.9% in 2015) in those above the threshold for depression and 20% decrease in
those reporting physically assaulting anyone at age 14 (40.1% in 2005, ~28% in 2015). Most
antisocial behaviours reported were substantially lower in 2015 compared to 2005 and there
was a sex interaction whereby the cohort difference was larger in females. Trends in
externalising behaviours have been understudied in cohort comparisons and this data
provides clear evidence for changes in antisocial behaviours in the decade between these
cohorts.
The increase in internalising mental health problems was consistent by sex, suggesting that
increases in psychological distress and self-harming behaviour are not increasing at higher
rates in females. This finding is in contrast to some other studies of adolescent trends that
indicated that increases in internalising problems were more consistent and greater in
females.6, 9, 11 For instance, previous research has reported odds in 2006 compared to 1986
at age 16 of 0.9 in males and 1.5 in females,6 compared to the increased odds in this study
of ~1.8 in both males and females in 2015 compared to 2005. It is striking that the rate of
increase of high depressive symptoms is more than 60% in just one decade. Poor mental
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health at this age predicts a host of lifelong negative consequences such as poorer health,
social and economic outcomes,3, 30 and therefore this sharp increase should cause concern.
Results for health related behaviours were mixed with less young people having tried
alcohol, binge drinking, smoking and having sex by mid adolescence in 2015 but being more
likely to have later bedtimes and wake up earlier, sleep less than the recommended 8 hours
for adolescents31 and to perceive themselves as overweight and to have higher BMIs. It is
relevant to note that although fewer young people had tried smoking cigarettes in 2015,
there was no cohort difference in the proportion smoking weekly at this age, although in
absolute terms the number of individuals smoking weekly at age 14 was small
(approximately 2% in both cohorts). In terms of sex differences in these cohort effects, the
odds for some antisocial behaviours and ever trying alcohol in 2015 compared to 2005 were
even lower in females compared to males, indicating that for some of these behaviours the
decreasing prevalences over time were more marked in females. Some of these findings
(e.g. underage substance use, sexual activity) are in line with research that demonstrates a
decrease in ‘adult activities’ among adolescents in recent decades,32 however, this
explanation does not help understand shorter sleeptimes, lower anti-social behaviours and
poorer weight related outcomes observed in this study.
The health-related behaviours identified in this study are all known risk factors for mental ill-
health.13-15, 18 In some instances the increasing trends in risky health behaviours such as
decreasing sleep times, increasing weight, and perceived overweight status might help
explain the increasing mental health difficulties experienced by adolescents. Where the
trends are moving in opposite directions (substance use, antisocial behaviours), the
interpretation becomes more complicated. It may suggest that the associations between
these behaviours and mental health are not consistent over generations and might be
changing over time. This is important with regards to trying to identify causal risk factors for
poor adolescent health outcomes. Unexpected patterns such as those seen in our study,
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could indicate that associations between, for example, cannabis use and depression could
be due to residual confounding rather than true causality. However, other factors not
included in the study are also likely to have changed over the ten years of investigation,
which may also impact on these associations. Understanding the dynamic relationships
between health behaviours and mental health should be a priority as adolescent mental
health problems increase, in order to identify suitable targets for interventions to prevent this
upward trend from continuing.
In addition to effectively using two large contemporary birth cohort studies, the study makes
several methodological advancements in improving our understanding of changing trends in
UK adolescents. Variables in the two cohorts, where dissimilar, were carefully harmonised to
ensure comparisons could be made. Unfortunately, this harmonisation could not be achieved
for certain variables of interest (physical activity), which we were therefore unable to include.
Similarly, for other variables the harmonisation is imperfect either owing to different time
periods of reference in the questions (e.g. self-harm) or availability only at a slightly different
age in the ALSPAC cohort (e.g. sleep times) and this must be borne in mind when
interpreting findings. In both these cases, however, the direction of bias is likely to be an
underestimation of the increased poorer outcomes in 2015, for instance, with self-harm we
estimate lifetime prevalence in ALSPAC and previous year prevalence in MCS. Although
ALSPAC and MCS are large and detailed birth cohorts, one is a regional cohort (ALSPAC)
and one is a national cohort (MCS), which could bias our findings. However, regional
variation in these outcomes was estimated and was found to be minimal (<1% for mental
health, sex, weight variables, <3% for substance use and sleep). Albeit employing multiple
techniques to increase the comparability of the cohorts, it is possible that some of the
differences observed are due to changes in demographic composition over the decade,
differences in the study samples, or the different rates and predictors of attrition between the
two studies. The nationally representative estimates for the MCS at age 14 indicate that
across all the investigated variables the comparable estimates were slightly different from
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the nationally representative ones, highlighting the value of applying techniques to increase
the comparability of these cohorts, but at the same time limiting the generalisability of our
secular trend estimates to the UK as a whole. It is also important to note that missing data
was higher in ALSPAC than MCS, and although we conducted multiple imputation with
socio-demographic and all examined variables informing the imputation to reduce bias in
estimates, some estimates might remain biased due to unmeasured factors associated with
missingness and their potential association with our outcomes of interest. Finally, two of the
measures used in this study are psychometric surveys (SDQ and SMFQ) and our findings
assume measurement invariance for these. However we have not tested this, and there is a
possibility that the surveys are not measuring the same constructs across the two cohorts.
There are a number of implications highlighted by our findings. Most importantly, the rapidly
increasing prevalence of depressive symptoms, self-harm, parent-reported mental health
problems, obesity and lesser sleep in adolescents over the past decade is an important
finding, and the reasons why this has occurred need thorough investigation. Identifying
further factors that have changed over the decade that might have resulted in UK young
people having less support and being at higher risk should be undertaken as a public health
priority. A further implication arising from our findings is that while certain mental health
problems are increasing, other problems and health related behaviours, thought to predict
poor mental health, are decreasing. Understanding the nature of these associations and
their dynamic nature over time could be extremely valuable in identifying causal risk factors
for mental health and potential targets for interventions. Identifying explanations for these
high prevalences and changing time trends are key for preventing further increases in poor
mental health and health outcomes for future generations of young people.
To conclude, in a large well-powered study across two key UK birth cohorts born a decade
apart, depressive symptoms and self-harm behaviours have increased between 2005 and
2015. Adolescents are spending less time sleeping and have higher BMIs. In contrast, other
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health related behaviours such as substance use and antisocial behaviours have decreased
over the same time period, suggesting that links between mental health problems and health
related behaviours might be more complex and dynamic in nature than currently predicted.
The data provide important evidence to understand health behaviours in millennials and how
these are changing, permitting the planning of policy and public health provision.
Acknowledgments
We are extremely grateful to all the families who took part in this study, the midwives for their
help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer
and laboratory technicians, clerical workers, research scientists, volunteers, managers,
receptionists and nurses.
The authors are grateful for the cooperation of the Millennium Cohort Study families who
voluntarily participate in the study. They would also like to thank a large number of
stakeholders from academic, policy-maker and funder communities and colleagues at the
Centre for Longitudinal Studies involved in data collection and management.
We would also like to thank Dr David Bann (UCL) and Dr Andrew Jones (University of
Liverpool) for their helpful comments on an initial draft of this manuscript.
Funding
This work did not receive specific funding. The UK Medical Research Council and Wellcome
(Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A
comprehensive list of grants funding is available on the ALSPAC website
(http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).The
Millennium Cohort Study is supported by the Economic and Social Research Council and a
consortium of UK government departments. The funders had no role in study design, data
collection, data analysis, data interpretation, or writing of this report.
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Authors contributions
PP and SG, planned the study, analysed the data and prepared the manuscript for
publication. Both PP and SG have full access to the data presented in this report and act as
guarantors for the paper.
Declaration of Interests: All authors have completed the ICMJE uniform disclosure form
at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
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References
1. Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, et al. Our future: a Lancet commission on adolescent health and wellbeing. Lancet. 2016;387:2423-78.2. Kessler RC, Avenevoli S, Costello EJ, Georgiades K, Green JG, Gruber MJ, et al. Prevalence, persistence, and sociodemographic correlates of DSM-IV disorders in the National Comorbidity Survey Replication Adolescent Supplement. Arch Gen Psychiatry. 2012;69:372-80.3. Colman I, Murray J, Abbott RA, Maughan B, Kuh D, Croudace TJ, et al. Outcomes of conduct problems in adolescence: 40 year follow-up of national cohort. Bmj. 2009;338:a2981.4. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62:593-602.5. Fink E, Patalay P, Sharpe H, Holley S, Deighton J, Wolpert M. Mental health difficulties in early adolescence: A comparison of two cross-sectional studies in England from 2009 to 2014. J Adolesc Health. 2015;56:502-7.6. Collishaw S, Maughan B, Natarajan L, Pickles A. Trends in adolescent emotional problems in England: a comparison of two national cohorts twenty years apart. J Child Psychol Psychiatry. 2010;51:885-94.7. Twenge JM, Gentile B, DeWall CN, Ma D, Lacefield K, Schurtz DR. Birth cohort increases in psychopathology among young Americans, 1938–2007: A cross-temporal meta-analysis of the MMPI. Clinical psychology review. 2010;30:145-54.8. Hagquist C. Discrepant trends in mental health complaints among younger and older adolescents in Sweden: an analysis of WHO data 1985–2005. J Adolesc Health. 2010;46:258-64.9. Bor W, Dean AJ, Najman J, Hayatbakhsh R. Are child and adolescent mental health problems increasing in the 21st century? A systematic review. Aust N Z J Psychiatry. 2014;48:606-16.10. Patalay P, Fitzsimons E. Mental ill-health among children of the new century: trends across childhood with a focus on age 14. London: Centre for Longitudinal Studies, 2017.11. Mojtabai R, Olfson M, Han B. National Trends in the Prevalence and Treatment of Depression in Adolescents and Young Adults. Pediatrics. 2016;138.12. Agalioti-Sgompou V, Christie S, Fiorini P, Hawkins V, Hinchliffe S, Lepps H, et al. Smoking, Drinking and Drug Use Among Young People in England - 2014. Health and Social Care Information Centre, 2015.13. Winsler A, Deutsch A, Vorona RD, Payne PA, Szklo-Coxe M. Sleepless in Fairfax: the difference one more hour of sleep can make for teen hopelessness, suicidal ideation, and substance use. J Youth Adolesc. 2015;44:362-78.14. Sharpe H, Patalay P, Choo T-H, Wall M, Mason SM, Goldschmidt AB, et al. Bidirectional associations between body dissatisfaction and depressive symptoms from adolescence through early adulthood. Dev Psychopathol. 2017:1-12.15. Kelly Y, Patalay P, Montgomery S, Sacker A. BMI Development and Early Adolescent Psychosocial Well-Being: UK Millennium Cohort Study. Pediatrics. 2016:e20160967.16. Jerstad SJ, Boutelle KN, Ness KK, Stice E. Prospective reciprocal relations between physical activity and depression in female adolescents. J Consult Clin Psychol. 2010;78:268.17. Degenhardt L, Hall W, Lynskey M. Exploring the association between cannabis use and depression. 2003;98:1493-504.18. Farrell M, Howes S, Bebbington P, Brugha T, Jenkins R, Lewis G, et al. Nicotine, alcohol and drug dependence, and psychiatric comorbidity--results of a national household survey. Int Rev Psychiatry. 2003;15:50-6.
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19. Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, et al. Cohort profile: the ‘children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2013;42:111-27.20. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort Profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42:97-110.21. Connelly R, Platt L. Cohort profile: UK millennium Cohort study (MCS). Int J Epidemiol. 2014;43:1719-25.22. Fitzsimons E. Millennium Cohort Study Sixth Survey 2015-2016: User Guide (First Edition). London: Centre for Longitudinal Studies, 2017.23. Mostafa T, Ploubidis GB. Millennium Cohort Study, Sixth Survey 2015-2016: Technical report on response (Age 14). London: Centre for Longitudinal Studies, 2017.24. Angold A, Costello EJ, Messer SC, Pickles A, Winder F, Silver D. The development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. Int J Methods Psychiatr Res. 1995;5:237-49.25. Goodman R. The strengths and difficulties questionnaire: a research note. 1997;38:581-86.26. Dehejia RH, Wahba S. Propensity Score-Matching Methods For Nonexperimental Causal Studies. Rev Econ Stat. 2002;84:151-61.27. Hainmueller J. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Anal. 2012;20:25-46.28. Leuven E, Sianesi B. PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. In: Components SS, editor. S432001. revised 19 Jul 2012 ed: Boston College Department of Economics; 2003.29. Hainmueller J, Xu Y. Ebalance: A Stata package for entropy balancing. 2013.30. Goodman A, Joyce R, Smith JP. The long shadow cast by childhood physical and mental problems on adult life. Proceedings of the National Academy of Sciences of the United States of America. 2011;108:6032-7.31. Paruthi S, Brooks LJ, D'Ambrosio C, Hall WA, Kotagal S, Lloyd RM, et al. Recommended amount of sleep for pediatric populations: a consensus statement of the American Academy of Sleep Medicine. J Clin Sleep Med. 2016;12:785-6.32. Twenge JM, Park H. The Decline in Adult Activities Among U.S. Adolescents, 1976–2016. Child Dev. 2017;0.
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Figure 1. ORs (95% CI) for poorer outcomes in 2015 (MCS) vs. 2005 (ALSPAC). Unadjusted estimates and estimates using entropy balancing weights are both presented.
Figure 2. ORs (95% CI) for poorer outcomes in 2015 (MCS) vs. 2005 (ALSPAC) for males and females
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Table 1. Measures in ALSPAC and MCS for each domain and the harmonised variable Outcome ALSPAC (2005) MCS (2015) Harmonised variableDepressive symptoms
a 13-item Short Moods and Feelings Questionnaire
b 13-item Short Moods and Feelings Questionnaire
Total depressive symptoms score (continuous)yes/no variable based on clinical threshold >=12
Self-harm a (Measured at age 15)Over your whole lifetime have you ever tried to harm yourself or kill yourself?
b In the past year have you hurt yourself on purpose in any way?
0 Not self harmed1 Have self harmed
NOTE: ALSPAC is lifetime but MCS is in past year
Antisocial behaviours
b How often in the last year have you done any of the following? (categorical)- Not at all- Just once- 3-5 times- 6+ times
Hit kicked or punched someone on purpose
Been rowdy or rude in a public place so that people complained or you got in to trouble
Written things or sprayed paint on a property that did not belong to you
Deliberately damaged or destroyed property that did not belong to you
Taken something from a shop without paying for it
Other items included skipping school, breaking and stealing from various different places, carrying a knife or weapon for protection, setting fire, stealing a vehicle, not paying correct fare on public transport, using force to steal.
b In the last 12 months have you:(yes/no) Pushed or shoved /hit/slapped/punched someone?
Been noisy or rude in a public place so that peoplecomplained or got you into trouble?
Written things or spray painted on a building, fence or train or anywhere else where you shouldn’t have?
On purpose damaged anything in a public place that didn’t belong to you, for example by burning, smashing or breaking things like cars, bus shelters and rubbish bins?
Taken something from a shop without paying for it?
Other items included using a weapon on someone, stealing from someone, hacking into computers, sending computer viruses.
0 No1 Yes
Assault
Rowdy behaviour
Graffiti
Vandalism
Shoplifting
Parent rated difficulties
c (Measured at age 13)Strengths and difficulties Questionnaire
c Strengths and difficulties Questionnaire
5 continuous subscale scores5 yes/no variables based on the ‘abnormal’ cutoff
Subscales: emotional symptom, conduct problems, hyperactivity, peer problems, prosocial behaviour
Alcohol use a Have you ever tried alcohol with/without your parents’ permission? (yes/no)
What is the most alcoholic drinks you’ve had in a single evening? (continuous variable)
How many times have you done this in the last year? (continuous variable)
b Have you ever had an alcoholic drink? That is more than a few sips. (yes/no)
How many times have you had an alcoholic drink in the last 12 months? (7 response options from never to over 40)
Have you ever had five or more alcoholic drinks at a time? A
0 Never drank a whole drink1 Nothing in last 12 months/never drank 5 or more2 1-2 times drank 5 or more alcoholic drinks in one evening3 3 or more times drank 5 or more alcoholic drinks in one evening
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drink is half a pint of lager, beeror cider, one alcopop, a small glass of wine, or a measure of spirits. (yes/no)
How many times have you had five or more alcoholic drinks at a time in the last 12 months?- Never- 1-2 times- 3-5 times- 6-9 times- 10 or more time
Smoking frequency
a Frequency teenager has smoked cigarettes in the past 6 months:- 1-3 times; - >4 times; - once per week; - never.
Number of cigarettes smoked per week in the last 6 months for weekly users (continuous variable)
b Please read the following statements carefully and decide which one best describes you. Donot include electronic cigarettes (e-cigarettes).- I have never smoked cigarettes; - I have only ever tried smoking cigarettes once; - I used to smoke sometimes but I never smoke a cigarette now; - I sometimes smoke cigarettes now but I don’t smoke as many as one a week; - I usually smoke between one and six cigarettes a week; - I usually smoke more than six cigarettes a week.
0 Non smoker1 Occasional smoker, not weekly2 Smokes 1-6 cigarettes a week3 Smokes more than 6 cigarettes a week
Cannabis a Have you ever used cannabis? (yes/no)
b Have you ever tried any of the following things?Cannabis (also known as weed, marijuana, dope, hash or skunk)? (yes/no)
0 Never used cannabis1 Have tried cannabis
Other drugs a Teenager has been offered drugs? (yes/no)
<if yes to above> Teenager has used drugs other than cannabis to feel good/get high? (yes/no)
b Have you ever tried any of the following things?Any other illegal drug (such as ecstasy, cocaine, speed)? (yes/no)
0 Never used other drugs1 Have tried other drugs
BMI a Height and weight measured by interviewer
a Height and weight measured by interviewer
BMI derived from height and weightObese (0=no, 1=yes) derived using IOTF threshold
Weight perception
b How do you describe your weight?- Very underweight- Slightly underweight- About the right weight- Slightly overweight- Very overweight
b Which of these do you think you are?- Underweight- About the right weight- Slightly overweight- Very overweight
Perceive themselves:1 Underweight2 About the right weight3 Slightly overweight4 Very overweight
Sleep b (Measured at age 15)What time do you usually get in to be on school days? (continuous - reported in hours and minutes am/pm)
What time do you usually get into bed on weekend days?(continuous - reported in hours and minutes am/pm)
b About what time do you usually go to sleep on a school night?About what time do you usually go to sleep on the nights when you do not have school thenext day?- Before 9pm- 9 - 9.59pm- 10 - 10.59pm
4 categorical sleep variables:
Schoolday bedtimeNon schoolday bedtime
1 Before 9pm2 9 - 9.59pm3 10 - 10.59pm4 11pm - midnight5 After midnight
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What time do you usually wake up on school days?(continuous - reported in hours and minutes am/pm)
What time do you usually wake up on weekend days?(continuous - reported in hours and minutes am/pm)
- 11pm - midnight- After midnight
About what time do you usually wake up on a school day?- Before 6am- 6 - 6.59am- 7 - 7.59am- 8 - 8.59am- 9am or later
About what time do you wake up in the morning on the days when you do not haveschool?- Before 8am- 8 - 8.59am- 9 - 9.59am- 10 - 10.59am- 11 - 11.59am- Midday or later
(11 pm and later classified as late bedtime)
Schoolday waketime
1 Before 6am2 6 - 6.59am3 7 - 7.59am4 8 - 8.59am5 9am or later(before 7 am classified as early waketime)
Non schoolday waketime
1 Before 8am2 8 - 8.59am3 9 - 9.59am4 10 - 10.59am5 11 - 11.59am6 Midday or later(before 9 am classified as early waketime)
Sleep duration weekdays:We estimate a sleep duration variable based on these categories (category mid-points are used and for earliest and latest times we use half hour pre/post stated time). Sleep less than 8 hours on weekdays is classified as insufficient sleep based on guidelines for adolescent sleep durations.
Sexual intercourse
b Series of questions regarding intimate contact with someone else leading to: Have you had sexual intercourse with another person in the past year?
b Series of questions regarding intimate contact with someone else leading to: In the last 12 months have you had sexual intercourse with another young person?
0 have not had sex1 had sex
Physical activity
b Frequency data available on approx. 80 specific activities (riding a bike, skipping, gardening, walking the dog, cricket etc)
b On how many days in the last week did you do a total of at least an hour of moderate tovigorous physical activity? By moderate to vigorous we mean any physical activity that makes you get warmer, breathe harder and makes your heart beat faster, e.g. riding a bike, running, playing football, swimming, dancing, etc.- Every day- 5-6 days- 3-4 days- 1-2 days- Not at all
Not harmonised
a Measured via interview; b Measured via self completion; cParent reported
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Table 2. Descriptive statistics for mental health outcomes in 2005 (ALSPAC) and 2015 (MCS)
2005 2015
Domain Variable ALSPAC N=5627
MCS [nationally representative estimates), N=11318
MCS [propensity score matched)N=5627
MCS [entropy balanced)N= 11318
Mean or % [95% CI]
Mean or % [95% CI]
Mean or % [95% CI]
Mean or % [95% CI]
SMFQ score 4.93[4.81,5.05] 5.72 [5.57,5.86] 5.44 [5.28,5.59] 5.41 [5.10,5.73]Depressive symptoms % above clinical cutoff 9.0 [8.3,9.8] 16.4 [15.5,17.3] 14.7 [13.8,15.6] 14.8 [12.7,16.8]
Self-harm % Yes 11.9 [10.9,13.0] 15.4 [14.5,16.3] 14.8 [13.8,15.7] 14.4 [12.8,16.0]
% Assault 40.1 [38.5, 41.6] 31.6 [30.4, 32.7]
28.9 [27.8,30.2] 27.7 [25.5,29.8]
% Rowdy behaviour 11.5 [10.6,12.4] 14.1 [13.2,14.9] 12.5 [11.6,13.3] 12.1 [10.4,13.9]% Graffiti 9.9 [8.9,10.9] 2.8 [2.5,3.2] 2.4 [2.0,2.8] 1.6 [1.3,2.0]% Vandalism 6.2 [5.6,7.0] 3.6 [3.1,4.1] 2.9 [2.5,3.3] 2.2 [1.7,2.7]
Antisocial behaviours
% Shoplifting 8.0 [7.1,8.8] 3.6 [3.1,4.1] 3.0 [2.5,3.4] 2.5 [1.9,3.2]
Emotional symptoms 1.42[1.37,1.47] 2.08 [2.02,2.14] 2.02 [1.96,2.07] 2.03 [1.93,2.13]% above clinical cutoff 5.7 [5.1,6.3] 13.9 [13.0,14.8] 13.0 [12.2,13.9] 13.4 [11.6,15.2]Conduct problems 1.20[1.16,1.24] 1.53 [1.48,1.58] 1.39 [1.35,1.43] 1.44 [1.35,1.53]% above clinical cutoff 6.0 [5.3,6.6] 11.9 [11.0,12.7] 10.0 [9.2,10.8] 11.6 [9.8,13.4]Hyperactivity symptoms 2.85[2.79,2.91] 3.12 [3.06,3.18] 2.97 [2.91,3.04] 3.07 [2.95,3.19]% above clinical cutoff 6.2 [5.6,6.9] 10.0 [9.2,10.8] 8.7 [7.9,9.4] 9.7 [8.3,11.0]Peer problems 1.21[1.16,1.25] 1.82 [1.78,1.87] 1.73 [1.68,1.78] 1.85 [1.73,1.97]% above clinical cutoff 8.9 [8.1,9.7] 16.8 [15.8,17.8] 15.0 [14.1,15.9] 17.7 [15.3,20.1]Prosocial behaviours 8.26[8.21,8.31] 8.25 [8.20,8.30] 8.38 [8.34,8.43] 8.41 [8.32,8.49]
Parent reported difficulties [SDQ)
% above clinical cutoff 1.2 [0.9,1.5] 1.9 [1.6,2.3] 1.4 [1.1,1.7] 1.4 [0.9,1.9]
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Table 3. Descriptive statistics for health-related behaviours in 2005 (ALSPAC) and 2015 (MCS)
2005 2015Domain Variable ALSPAC
N=5627MCS [nationally representative estimates) N=11318
MCS [propensity score matched)N=5627
MCS [entropy balanced)N= 11318
Mean or % [95% CI]
Mean or % [95% CI]
Mean or % [95% CI]
Mean or % [95% CI]
Alcohol use % never drank 47.9 [46.6,49.2] 51.8 [50.6,53] 57.8 [56.5,59.1] 56.5 [53.9,59.0]% nothing past 12 mo, never heavy drinking
42.1 [40.8,43.4] 38.3 [37.1,39.5] 34.7 [33.4,35.9] 37.2 [34.7,39.8]
% heavy drinking 1-2 times in past 12 mo
6.7 [6,7.3] 6.2 [5.6,6.7] 4.8 [4.3,5.4] 3.9 [3.2,4.6]
% heavy drinking >3 times in past 12 mo
3.4 [2.9,3.8] 3.7 [3.2,4.2] 2.6 [2.2,3.1] 2.4 [1.7,3.1]
Smoking % non-smoker 90.8 [90,91.5] 95.3 [94.7,95.9] 97.0 [96.6,97.5] 97.1 [96.4,97.8]% occasional, not weekly 7.2 [6.5,7.8] 2.1 [1.7,2.5] 1.4 [1.1,1.7] 1.2 [0.8,1.6]% 1-6 cigarettes/week 0.6 [0.4,0.8] 0.9 [0.7,1.1] 0.6 [0.4,0.8] 0.4 [0.2,0.7]%>6 cigarettes/week 1.4 [1.1,1.7] 1.7 [1.4,2.1] 1.0 [0.8,1.3] 1.3 [0.7,1.9]
Cannabis % tried 4.6 [4.0,5.1] 5.5 [4.9,6.1] 3.7 [3.2,4.2] 3.9 [2.6,5.2]
Other drugs % tried 0.4 [0.2,0.6] 0.8 [0.6,1.0] 0.6 [0.4,0.8] 0.8 [0.3,1.2]
Weight Mean BMI 20.32[20.23,20.41] 21.58[21.47,21.69] 21.36[21.25,21.47] 21.25[21.07,21.44]% obese 3.8 [3.3,4.3] 7.8 [7.1,8.5] 7.4 [6.7,8.0] 7.3 [6.1,8.5]
% underweight 14.3 [13.2,15.4] 7.0 [6.4,7.6] 6.9 [6.2,7.6] 6.8 [5.7,7.9]Weight Perception % about right weight 59.3 [57.9,60.7] 59.4 [58.2,60.5] 60.3 [59.1,61.6] 60.2 [57.7,62.8]
% overweight 23.0 [21.8,24.2] 28.7 [27.6,29.8] 28.4 [27.3,29.6] 28.8 [26.3,31.4]% very overweight 3.5 [2.9,4.0] 4.9 [4.4,5.5] 4.3 [3.7,4.8] 4.1 [3.3,4.9]
before 9pm 0.9 [0.6,1.2] 5.0 [4.5,5.6] 4.9 [4.4,5.5] 6.0 [4.8,7.1]9-9.59 pm 15.3 [14.3,16.4] 29.2 [28.1,30.3] 30.1 [28.9,31.2] 33.1 [30.6,35.7]10-10.59 pm 64.9 [63.4,66.4] 39.7 [38.5,40.9] 39.9 [38.7,41.2] 37.0 [34.7,39.3]11-midnight 16.3 [15.2,17.4] 19.6 [18.6,20.5] 19.3 [18.3,20.4] 18.0 [16.0,20.0]
Sleep- Schoolday bedtime
after midnight 2.6 [2.1,3.0] 6.5 [5.8,7.1] 5.7 [5.1,6.3] 5.9 [4.8,7.1]before 9pm 0.2 [0.1,0.3] 0.9 [0.6,1.1] 0.9 [0.6,1.1] 0.9 [0.4,1.3]9-9.59 pm 2.7 [2.2,3.2] 5.6 [5.1,6.1] 6.1 [5.4,6.7] 7.3 [5.6,9]10-10.59 pm 24.1 [22.9,25.3] 23.4 [22.4,24.4] 24.8 [23.7,26.0] 27.5 [25.3,29.8]11-midnight 42.3 [40.8,43.8] 36.2 [35.1,37.4] 36.8 [35.5,38.1] 33.7 [31.4,36.0]
Sleep- Non schoolday bedtime
after midnight 30.7 [29.4,32.1] 33.9 [32.7,35.0] 31.4 [30.2,32.6] 30.5 [28.2,32.8]before 6am 1.0 [0.7,1.3] 4.4 [3.9,5] 3.6 [3.1,4.0] 3.5 [2.6,4.5]6-6.59 am 28.4 [27.0,29.8] 42.3 [41.1,43.5] 38.8 [37.5,40.1] 38.4 [36.0,40.7]7-7.59 pm 66.1 [64.7,67.5] 49.4 [48.2,50.6] 53.5 [52.2,54.8] 54.4 [51.9,56.9]8-8.59am 4.2 [3.7,4.8] 3.1 [2.7,3.5] 3.5 [3.0,4.0] 3.2 [2.5,4.0]
Sleep- Schoolday waketime
after 9am 0.3 [0.1,0.4] 0.9 [0.5,1.2] 0.6 [0.4,0.9] 0.5 [0.2,0.7]before 8am 6.5 [5.8,7.3] 8.1 [7.4,8.7] 7.8 [7.1,8.5] 8.1 [6.6,9.6]8-8.59 am 15.4 [14.3,16.5] 16.2 [15.3,17.1] 15.8 [14.9,16.8] 17.0 [14.9,19.0]9-9.59 pm 26.2 [24.8,27.5] 24.8 [23.8,25.8] 25.4 [24.2,26.5] 25.6 [23.4,27.9]10-10.59am 31.1 [29.7,32.5] 29.0 [27.9,30.1] 29.8 [28.6,31.0] 27.9 [25.7,30.1]11-11.59am 15.1 [13.9,16.3] 15.0 [14.1,15.8] 14.8 [13.8,15.7] 15.2 [13.5,16.9]
Sleep- Non schoolday waketime
after midday 5.8 [5.1,6.5] 6.9 [6.3,7.6] 6.4 [5.8,7.0] 6.1 [4.9,7.4]Mean sleep duration 8.70 [8.68,8.73] 8.60 [8.58,8.63] 8.68 [8.65,8.71] 8.74 [8.68,8.79] Sleep duration
(weekday) % sleep less than 8 hours 5.7 [5.1,6.4] 13.4 [12.6,14.3] 11.6 [10.7,12.4] 11.5 [10.0,13.1]
Sexual intercourse
% yes 2.1 [1.8,2.5] 2.0 [1.7,2.4] 1.2 [0.9,1.5] 0.9 [0.6,1.3]
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Table 4. Descriptive statistics for mental health and health related behaviours in 2005 (ALSPAC) and 2015 (MCS) by sex
2005 ALSPAC 2015 MCS
Domain VariableMales Females Males Females
Mean or % [95% CI] Mean or % [95% CI] Mean or % [95% CI] Mean or % [95% CI]SMFQ score 4.14 [3.99,4.28] 5.69 [5.51,5.87] 4.22 [4.05,4.39] 7.31 [7.08,7.54]Depressive
symptoms % above clinical cutoff 5.65 [4.78,6.52] 12.4 [11.1,13.6] 9.19 [8.13,10.3] 24.0 [22.5,25.4]
Self-harm % Yes 6.86 [5.67,8.05] 16.9 [15.2,18.6] 8.49 [7.51,9.47] 22.8 [21.3,24.2]
% Assault 48.1 [45.9,50.3] 32.3 [30.3,34.4] 41.4 [39.7,43.1] 21.1 [19.7,22.5]% Rowdy behaviour 11.0 [9.63,12.3] 12.0 [10.7,13.4] 15.3 [14.0,16.5] 12.7 [11.6,13.9]% Graffiti 9.01 [7.56,10.4] 10.7 [9.38,12.0] 3.17 [2.61,3.73] 2.47 [1.95,2.98]% Vandalism 7.70 [6.35,9.06] 4.73 [3.79,5.67] 4.44 [3.66,5.22] 2.76 [2.12,3.40]
Antisocial behaviours
% Shoplifting 7.60 [6.44,8.76] 8.30 [7.14,9.46] 4.36 [3.67,5.04] 2.81 [2.25,3.38]
Emotional symptoms 1.23 [1.16,1.30] 1.60 [1.52,1.67] 1.78 [1.69,1.86] 2.41 [2.33,2.48]% above clinical cutoff 4.70 [3.89,5.50] 6.67 [5.73,7.61] 10.5 [9.35,11.7] 17.4 [16.1,18.8]Conduct problems 1.24 [1.18,1.30] 1.16 [1.10,1.22] 1.60 [1.53,1.67] 1.46 [1.40,1.51]% above clinical cutoff 6.42 [5.47,7.36] 5.55 [4.67,6.43] 13.7 [12.3,15.1] 9.89 [8.79,11.0]Hyperactivity symptoms 3.28 [3.19,3.37] 2.44 [2.36,2.52] 3.54 [3.45,3.63] 2.67 [2.59,2.75]% above clinical cutoff 8.81 [7.72,9.90] 3.69 [2.97,4.42] 13.4 [12.1,14.6] 6.41 [5.48,7.34]Peer problems 1.36 [1.29,1.43] 1.06 [0.99,1.12] 1.92 [1.85,1.99] 1.73 [1.67,1.79]% above clinical cutoff 11.2 [9.98,12.4] 6.71 [5.75,7.67] 18.5 [17.0,20.0] 14.9 [13.7,16.1]Prosocial behaviours 8.01 [7.93,8.08] 8.50 [8.43,8.57] 8.02 [7.95,8.09] 8.49 [8.42,8.56]
Parent reported difficulties [SDQ)
% above clinical cutoff 1.67 [1.17,2.18] 0.72 [0.39,1.05] 2.35 [1.78,2.92] 1.51 [1.03,1.99]
Alcohol use % never drank 48.7 [46.8,50.6] 47.1 [45.2,48.9] 51.3 [49.6,53.0] 52.4 [50.7,54.1]% nothing past 12 mo, never heavy drinking
40.7 [38.9,42.6] 43.4 [41.6,45.3] 39.4 [37.7,41.1] 37.2 [35.6,38.8]
% heavy drinking 1-2 times in past 12 mo
7.35 [6.37,8.33] 6.00 [5.11,6.89] 6.07 [5.26,6.88] 6.24 [5.38,7.10]
% heavy drinking >3 times in past 12 mo
3.24 [2.58,3.91] 3.50 [2.81,4.19] 3.26 [2.60,3.92] 4.20 [3.48,4.92]
Smoking % non-smoker 93.2 [92.2,94.1] 88.4 [87.3,89.6] 96.3 [95.6,97.0] 94.1 [93.2,95.0]% occasional, not weekly
5.15 [4.33,5.98] 9.14 [8.08,10.2] 1.61 [1.11,2.10] 2.59 [1.98,3.19]
% 1-6 cigarettes/week 0.44 [0.19,0.69] 0.81 [0.48,1.14] 0.68 [0.39,0.97] 1.13 [0.75,1.51]%>6 cigarettes/week 1.24 [0.83,1.65] 1.61 [0.12,2.08] 1.39 [0.92,1.86] 2.15 [1.54,2.76]
Cannabis % tried 5.32 [4.48,6.16] 3.86 [3.15,4.57] 5.57 [0.47,0.64] 5.43 [4.57,6.29]
Other drugs % tried 0.45 [0.18,0.72] 0.34 [0.12,0.55] 0.81 [0.48,1.14] 0.79 [0.49,1.10]
Weight Mean BMI 19.9 [19.8,20.1] 20.7 [20.6,20.8] 21.0 [20.9,21.2] 22.2 [22.0,22.3]% obese 3.93 [3.21,5.66] 3.66 [2.97,4.35] 7.64 [6.66,8.61] 7.92 [6.97,8.87]
% underweight 17.3 [15.6,18.9] 11.4 [10.1,12.7] 9.36 [8.33,10.4] 4.58 [3.87,5.28]Weight Perception % about right weight 61.1 [59.1,63.1] 57.4 [55.4,59.5] 63.7 [62.0,65.3] 54.8 [53.2,56.5]
% overweight 19.4 [17.8,21.0] 26.4 [24.7,28.2] 23.6 [22.2,25.1] 34.0 [32.4,35.6]% very overweight 2.20 [1.54,2.85] 4.68 [3.83,5.53] 3.34 [2.67,4.00] 6.59 [5.72,7.45]
before 9pm 0.86 [0.47,1.26] 0.90 [0.52,1.28] 5.44 [4.64,6.23] 4.61 [3.88,5.35]Sleep- Schoolday 9-9.59 pm 14.8 [13.3,16.2] 15.9 [14.5,17.3] 28.5 [27.0,30.0] 29.9 [28.4,31.4]
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10-10.59 pm 64.7 [62.5,67.0] 65.1 [63.2,67.1] 40.5 [38.8,42.2] 38.9 [37.3,40.6]11-midnight 16.9 [13.1,18.6] 15.7 [14.2,17.2] 18.9 [17.5,20.2] 20.3 [19.0,21.7]
bedtime
after midnight 2.76 [2.10,3.42] 2.37 [1.74,3.01] 6.74 [5.76,7.73] 6.21 [5.37,7.06]before 9pm 0.18 [0.00,0.35] 0.22 [0.02,0.42] 0.88 [0.57,1.20] 0.82 [0.51,1.14]9-9.59 pm 2.45 [1.78,3.11] 2.92 [2.18,3.65] 5.89 [5.09,6.68] 5.30 [4.58,6.03]10-10.59 pm 22.5 [20.6,24.4] 25.7 [24.0,27.4] 22.5 [21.1,23.9] 24.4 [23.0,25.8]11-midnight 41.6 [39.4,43.7] 43.0 [40.8,45.1] 35.2 [33.6,36.8] 37.3 [35.7,38.9]
Sleep- Non schoolday bedtime
after midnight 33.3 [31.3,35.3] 28.2 [26.3,30.1] 35.5 [33.8,27.2] 32.1 [30.5,33.7]before 6am 0.85 [0.47,1.21] 1.10 [0.68,1.53] 4.40 [3.61,5.18] 4.50 [3.77,5.24]6-6.59 am 23.7 [21.7,25.7] 33.0 [31.1,34.8] 36.9 [35.2,38.6] 48.0 [46.3,49.7]7-7.59 pm 69.2 [67.2,71.1] 63.1 [61.2, 65.0] 53.6 [51.8,55.3] 44.9 [43.2,46.5]8-8.59am 5.91 [4.88,6.94] 2.63 [1.97,3.29] 4.02 [3.36,4.67] 2.05 [1.59,2.51]
Sleep- Schoolday waketime
after 9am 0.36 [0.10,0.63] 0.19 [0.00,0.39] 1.10 [0.46,1.74] 0.59 [0.35,0.84]before 8am 7.70 [6.58,8.83] 5.36 [4.42,6.31] 9.74 [8.70,10.8] 6.31 [5.49,7.13]8-8.59 am 15.5 [13.9,17.0] 15.3 [13.9,16.8] 16.8 [15.5,18.1] 15.5 [14.3,16.8]9-9.59 pm 25.4 [23.4,27.4] 26.9 [25.0,28.7] 23.1 [21.7,24.5] 26.6 [25.1,28.0]10-10.59am 29.9 [27.9,31.9] 32.3 [30.3,34.2] 28.0 [26.4,29.5] 30.1 [28.6,31.7]11-11.59am 15.2 [13.8,16.6] 14.9 [13.4,16.4] 14.7 [13.5,16.0] 13.2 [13.0,16.5]
Sleep- Non schoolday waketime
after midday 6.34 [5.35,7.32] 5.29 [4.37,6.20] 7.67 [6.58,8.76] 6.19 [5.37,7.00]Mean sleep duration 8.75 [8.72,8.79] 8.65 [8.62,8.68] 8.78 [8.71,8.86] 8.70 [8.61,8.78] Sleep
duration (weekday)
% sleep less than 8 hours
5.0 [4.0,5.9] 6.5 [5.5, 7.5] 11.0 [8.8,13.2] 12.0 [9.9,14.1]
Sexual intercourse
% yes 2.3 [1.7,2.8] 2.0 [1.5,2.6] 2.0 [1.4,2.5] 2.1 [1.6,2.6]
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Figure 1: ORs (95% CI) for poorer outcomes in 2015 (MCS) vs. 2005 (ALSPAC). Unadjusted estimates and estimates using entropy balancing weights are both presented.
503x335mm (72 x 72 DPI)
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Figure 2. ORs (95% CI) for poorer outcomes in 2015 (MCS) vs. 2005 (ALSPAC) for males and females
499x333mm (72 x 72 DPI)
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